亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Potential of lidar sensors for the detection of UAVs

激光雷达 目标检测 遥感 计算机视觉 雷达 人工智能 计算机科学 雷达跟踪器 跟踪(教育) 测距 模式识别(心理学) 地理 电信 心理学 教育学
作者
Marcus Hammer,Marcus Hebel,Björn Borgmann,Martin Laurenzis,Michael Arens
标识
DOI:10.1117/12.2303949
摘要

The number of reported incidents caused by UAVs, intentional as well as accidental, is rising. To avoid such incidents in future, it is essential to be able to detect UAVs. LiDAR systems are well known to be adequate sensors for object detection and tracking. In contrast to the detection of pedestrians or cars in traffic scenarios, the challenges of UAV detection lie in the small size, the various shapes and materials, and in the high speed and volatility of their movement. Due to the small size of the object and the limited sensor resolution, a UAV can hardly be detected in a single frame. It rather has to be spotted by its motion in the scene. In this paper, we present a fast approach for the tracking and detection of (low) flying small objects like commercial mini/micro UAVs. Unlike with the typical sequence -track-after-detect-, we start with looking for clues by finding minor 3D details in the 360° LiDAR scans of scene. If these clues are detectable in consecutive scans (possibly including a movement), the probability for the actual detection of a UAV is rising. For the algorithm development and a performance analysis, we collected data during a field trial with several different UAV types and several different sensor types (acoustic, radar, EO/IR, LiDAR). The results show that UAVs can be detected by the proposed methods, as long as the movements of the UAVs correspond to the LiDAR sensor's capabilities in scanning performance, range and resolution. Based on data collected during the field trial, the paper shows first results of this analysis.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI6.2应助悠悠采纳,获得10
2秒前
21秒前
英姑应助Marshall采纳,获得10
59秒前
脑洞疼应助科研通管家采纳,获得10
1分钟前
1分钟前
QYQ完成签到 ,获得积分10
1分钟前
悠悠发布了新的文献求助10
1分钟前
1分钟前
胡萝卜完成签到,获得积分10
1分钟前
MchemG应助TXZ06采纳,获得30
2分钟前
2分钟前
xiaoqingnian完成签到,获得积分10
2分钟前
科研通AI6.1应助靤君采纳,获得30
2分钟前
andy完成签到,获得积分10
2分钟前
3分钟前
靤君发布了新的文献求助30
3分钟前
悠悠发布了新的文献求助10
3分钟前
4分钟前
聪明怜阳发布了新的文献求助10
4分钟前
科研通AI6.4应助gulibaier采纳,获得10
4分钟前
情怀应助pete采纳,获得10
5分钟前
5分钟前
深情安青应助科研通管家采纳,获得30
5分钟前
Marshall发布了新的文献求助10
5分钟前
Marshall完成签到,获得积分10
5分钟前
陶醉的蜜蜂完成签到,获得积分10
5分钟前
5分钟前
5分钟前
5分钟前
gulibaier发布了新的文献求助10
6分钟前
6分钟前
pete发布了新的文献求助10
6分钟前
羟基磷酸钙完成签到 ,获得积分10
6分钟前
6分钟前
bkagyin应助坦率的丹烟采纳,获得10
6分钟前
6分钟前
6分钟前
6分钟前
7分钟前
优雅柏柳发布了新的文献求助10
7分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Organometallic Chemistry of the Transition Metals 800
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6440843
求助须知:如何正确求助?哪些是违规求助? 8254674
关于积分的说明 17571909
捐赠科研通 5499112
什么是DOI,文献DOI怎么找? 2900088
邀请新用户注册赠送积分活动 1876663
关于科研通互助平台的介绍 1716916